As widespread use of mobile media devices continues to increase, there remains a pressing desire for an effective means of resizing images to fit arbitrary screen sizes. Traditional methods such as cropping or resampling either remove important image features or introduce significant visual distortion. Initially, a content-aware image resizing technique called seam carving that iteratively removes/adds connected paths of pixels to achieve a desired target size. This was followed by the development of several other techniques which employed global resizing rather than the iterative method used in seam carving. While these techniques produced good results, they are less scalable—an image must be reprocessed for each resolution change. Seam carving on the other hand enables one to store the locations of all seam paths removed/added in achieving some minimum/maximum resolution, allowing any resolution in between to be achieved by simply recalling the path locations and removing/repeating those pixels. For this reason and others, seam carving remains an effective image resizing tool. Although there has been improvement on the original seam carving technique (as well as extending it to video), there are still cases where seam carving has problems.
Many of the problems with traditional seam carving occur when multiple objects and/or textures exist within the image. Often the seam carving algorithm will misinterpret the energy data, conclude that a seam is unimportant, and remove a seam that actually is a portion of the main object in the image. This results in the removal or distortion of important features in the image. The present disclosure solves this problem by highlighting the important objects in the image and removing seams that do not interfere with the display of these objects.
This section provides background information related to the present disclosure which is not necessarily prior art.